2D-CNN MVS Depth Estimation.    Even thought 3D convolutional methods usually deliver high accuracy, but they demand a large amount of memory footprint and computation cost. Instead, some methods, e.g., MVDepthNet [1]} and DeepVideoMVS [2]}, generate 3D volumes by computing correlation or dot production between the extracted features of multi-view input images. The 3D cost volumes are further regularized by 2D convolutions. DeepVideoMVS [2]} extracts multi-scale features. It uses the feature at half scale to construct the cost volume, and other features as the skip connections to a series of decoders for depth regression. PatchmatchNet [4]} proposes an adaptive procedure mimicking PatchMatch [5]} to achieve superior efficiency. 2D convolutions are much faster and more memory efficient than the 3D counterparts, making them better suitable for lightweight networks in real-time applications.
